The Second MLC-SLM Challenge: Multilingual Conversational Speech Diarization, Recognition, and Understanding

2026-09-23

AI Summary

本文总结了旨在推进多语种对话语音模型发展的MLC-SLM挑战赛，通过两个任务：多语种对话语音识别与理解，并基于参赛系统提炼了有效方法。

Abstract

This paper summarizes the Interspeech2026 second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge, which aims to advance the development of effective multilingual conversational speech language models. We describe the two challenge tasks: multilingual conversational speech diarization and recognition, and multilingual conversational speech understanding, together with the released real-world conversational speech dataset, evaluation protocols, and baseline systems. The challenge attracted 91 teams worldwide, with 704 valid leaderboard results and 14 technical reports across the two tasks. Based on the participating systems, we summarize representative approaches and distill practical insights into multilingual conversational speech recognition and understanding to support future research in the community.

Problem

• Multilingual
• Conversational Speech
• Diarization
• Recognition
• Understanding

Innovation

• Multilingual Conversational Speech
• Diarization and Recognition
• Understanding
• Real-world Dataset
• Baseline Systems